Football in action: the matches the AI analyses every day.
An AI for soccer does not predict winners — it estimates probabilities. It processes recent form, expected goals (xG), injuries, likely line-ups and the bookmaker's odds, then returns a number: 57% home, 27% draw, 16% away. What makes that useful is not the number itself but comparing it against what the market pays. Our model has hit 59.7% across 809 settled picks — and that figure swings a lot depending on the league.
Why soccer is the hardest sport to model
Goals. A basketball game is decided over roughly two hundred points, so variance averages out and the better team usually wins. A soccer match is decided by two or three goals, and one of them can go in off a shin in the 93rd minute. With that few scoring events, luck never washes out.
The practical consequence: in soccer the edge is thin and it only pays off over hundreds of bets. Any tool promising 80% accuracy is either measuring it wrong or lying.
What the model actually looks at
Recent form, weighted by opponent quality — five wins against the bottom half are worth less than two draws against the top. Expected goals (xG), which correct for luck: a side that lost 1-0 while generating 2.4 xG played well and lost, and that predicts the next match better than the scoreline does. Absences, weighted unevenly, because losing your starting striker is not losing your fourth-choice centre-back. And market odds, the hardest benchmark of all, because they already contain the money of thousands of informed bettors.
MLS is the toughest league we cover — by design
Our MLS hit rate is 16 of 28 (57%), near the bottom of every competition we track. That is not a flaw in the league, it is the league working as intended: the salary cap, the draft and the allocation rules deliberately compress the talent gap. When no team is much better than another, there is less mispricing to find.
Compare it with the Bundesliga at 69% (20 of 29), where high pressing and open games make the goal markets far more legible. Same model, same method, twelve points apart.
If you follow European soccer from the US
The two leagues most watched from North America behave very differently for betting purposes:
- Premier League — 22 of 42 (52%): our weakest big-five league. It has the most money, the most analysts and the sharpest prices on the planet, which is exactly why edge is hardest to find there.
- LaLiga — 30 of 51 (59%): better, and with a clear pattern — Both teams to score runs at 69% while Over goals sits at 48%.
That gap is the whole point: the market that pays off changes with the competition. In the Copa Libertadores the goal markets hit 73%; in Serie A they collapse to 29%. Applying one strategy across every league is the most expensive mistake in soccer betting.
The number nobody else publishes
Hit rate is the easy metric. The one that decides whether any of this makes money is yield — and ours is 1.04%. Essentially break-even, with 59.5% accuracy.
That means the model separates matches well but the bet sizing eats the edge. We publish it because a site that only shows you its winners is useless for deciding anything, and because no paid tool will ever show you this number.
How to actually use it
Divide 1 by the odds to get the implied probability. Odds of 1.90 imply 52.6%. If the model says 62%, there are 9.4 points of edge and the bet makes sense. If the odds are 1.55 — implying 64.5% — there is nothing there, however much you like the team. That comparison is the entire game, and it means passing on most matches.
Keep reading
- ⚽ AI for football: what it analyzes — the same ground with the British term.
- 🏆 AI Premier League predictions — the toughest league, in detail.
- 💰 Value bet calculator — the implied-probability maths, done for you.
- 📋 The full verified record — every pick with its odds and result, in JSON and CSV.